1. Field of the Invention
The present invention relates generally to optical character recognition (OCR) within the word and sentence contexts of the character. More particularly, the invention relates to a method of text recognition based on both the input image patterns using an auto-associative neural network model, and the contexts of words and sentences using the cogent confabulation model.
2. Description of the Background Art
Military planning, battlefield situation awareness, and strategic reasoning rely heavily on the knowledge of the local situation and the understanding of different cultures. A rich source of such knowledge is presented as natural language text. In 2009, DARPA launched the Machine Reading program to develop a universal text-to-knowledge engine that scavenges digitized text to generate knowledge that can be managed by artificial intelligence reasoning systems. The Machine Reading program limited its scope to text available on the World Wide Web. In real life, text exists in many forms other than ASCII representation. These include printed texts such as books, newspapers and bulletins, as well as hand written texts. There are many occasions when only the scanned or photographed image of the texts is available for computer processing. While machine reading systems bridge the gap between natural language and artificial intelligence, another bridge has to be constructed to link the natural state of texts to a unique encoding that can be understood by computers.
Prior art conventional Optical Character Recognition (OCR) tools or pattern recognition techniques are not enough to meet the challenges in general applications of text extraction. Because the text images are sometimes captured under extreme circumstances, sometimes the images will be noisy, or incomplete due to damages to the printing material, or obscured by marks or stamps. Pattern recognition is extremely difficult, if not impossible, when the image is partially shaded or partially missing. However, such tasks are not too difficult for humans as we predict the missing information based on its context. Most human cognitive processes involve two interleaved steps, perception and prediction. Together, they provide higher accuracy.